Support of decision making by data mining using neural system
Support of decision making by data mining using neural system
复制标题
使用神经系统进行数据挖掘支持决策
DOI：
10.1002/scj.10577
复制
发表时间：
2005
期刊：
影响因子：
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通讯作者：
M. Uchida
中科院分区：
文献类型：
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作者：
K. Okuhara;H. Ishii;M. Uchida
本文以果树周围的环境因子为被解释变量，以农产品质量因子为被解释变量。对观测数据进行了分析。分析的对象是温州橙子（柑橘橙子）。本文的目的是分析环境因素对产品质量的影响，为生产出高质量的产品而对可控环境因素设定目标值，并对各项目的栽培环境进行评价。提出了一个数据分析系统，该系统从观察到的数据中提取用于决策（例如种植管理以及收获/运输计划）的有用信息。该系统采用概率神经网络，学习由观测数据组成的描述变量到解释变量的映射。通过叠加所获得的知识，可以执行逆建模。结果表明，利用该系统，可以实现多元非线性逆映射，而不是传统的一元回归分析中使用的线性逆回归估计。© 2005 Wiley Periodicals，Inc. Syst Comp Jpn，36（11）：102-110，2005;在线发表于Wiley InterScience（www.interscience. wiley.com）。DOI 10.1002/scj.10577
In this paper, the environmental factors surrounding fruit trees are used as the explanatory variables, and the factors concerning the quality of the agricultural produce are used as the explained variables. The observed data are analyzed. The object of analysis is the Onshu orange (mandarin orange). The purposes of this paper are analysis of the effect of environmental factors on product quality, the target value settings for the controllable environmental factors in order to produce high-quality products, and evaluation of the cultivation environment for each item. A system for data analysis is proposed which extracts the useful information for the decision-making such as cultivation management, as well as harvest/shipping planning, from the observed data. The system uses a probabilistic neural network and learns the mapping from the describing variables consisting of observed data to the explained variables. By superposing the acquired knowledge, inverse modeling can be performed. It is shown that by using the proposed system, multivariate nonlinear inverse mapping can be realized instead of the linear inverse regression estimation used in conventional single regression analysis. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(11): 102–110, 2005; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/scj.10577